Dear Douglas,

Many thanks for your explanations!   The actual model I am fitting to the
data is as follows:

Followup_ij = beta0 + beta1*Condition_j  + beta2*Baseline_ij + u_j +
epsilon_ij

where i  is an index denoting a student and  j is an index denoting that
student's school
(half of the schools were allocated to an intervention, and the other half
to control).

I fitted this model to the data in two different ways:

model.1f.lme <- lme(Followup ~ 1 + Baseline + Condition, random=~1|School,
                           method="REML", data=dat.miss)

model.0f.lme <- lme(Followup ~ 0 + Baseline + Condition, random=~1|School,
                           method="REML", data=dat.miss)

I understand that the coefficient of Condition in model.1f.lme (where
Condition is a factor having the levels Intervention, coded as 1,
and Control, coded as 0) is supposed to represent the intervention effect.

What confuses me is that the coefficients corresponding to the two levels of
Condition in model.0f.lme are reported as the estimated
values of  beta0 and beta0 + beta1, respectively, as derived from
model.1f.lme.    My interest is also in reporting the mean value of
the outcome (Followup) for the Intervention and Control and it doesn't seem
like the "+0" trick will get me close to this goal, as it
ignores the Baseline factor included in the model.  I am wondering if you
have any thoughts on how to get these mean values out of R?


Thank you!

Isabella




  ----- Forwarded Message ----
  From: Douglas Bates <bates@stat.wisc.edu>
  To: Isabella Ghement <isabella@ghement.ca>
  Cc: r-sig-mixed-models@r-project.org
  Sent: Wed, February 3, 2010 10:07:17 AM
  Subject: Re: [R-sig-ME] Baseline + Follow-Up

  On Wed, Feb 3, 2010 at 11:46 AM, Isabella Ghement <isabella@ghement.ca>
wrote:
  > Thank you so much, Douglas!  I am not familiar with the "0" trick - can
you
  > provide me with
  > some intuition for why this might work?

  The (Intercept) term is implicit in a linear model formula.  Thus the
formula

  FollowUp ~ Treatment + Baseline

  is equivalent to

  FollowUp ~ 1 + Treatment + Baseline

  The symbolic analysis of the formula and data detects that there is an
  intercept term and expresses Treatment as one contrast, which happens
  to be the difference between treatment B and treatment A.  The 0 + in
  the formula

  FollowUp ~ 0 + Treatment + Baseline

  suppresses the intercept term.  The first factor in the formula is
  then expressed as a set of indicator columns, producing separate
  estimates for treatment A and treatment B.

  >
  > Isabella
  >
  >
  > -----Original Message-----
  > From: dmbates@gmail.com [mailto:dmbates@gmail.com]On Behalf Of Douglas
  > Bates
  > Sent: February 3, 2010 9:40 AM
  > To: Isabella Ghement
  > Cc: r-sig-mixed-models@r-project.org
  > Subject: Re: [R-sig-ME] Baseline + Follow-Up
  >
  >
  > On Wed, Feb 3, 2010 at 11:12 AM, Isabella Ghement <isabella@ghement.ca>
  > wrote:
  >> Hi everyone,
  >>
  >> I am using the lme() function in the nlme library to fit a linear mixed
  >> effects model
  >> such as the one below, where an individual-level follow-up measurement
is
  >> modeled as a linear
  >> function of an individual-level baseline measurement plus a fixed
  > treatment
  >> effect
  >> plus a random cluster effect plus random error:
  >>
  >> lme(FollowUp ~ Baseline + Treatment, random=~1|Cluster, method="REML",
  >> data=Data)
  >>
  >> The summary of the lme model fit reports a point estimate and
confidence
  >> interval
  >> for the treatment effect (i.e., difference in adjusted treatment
means),
  > but
  >> I am also
  >> interested in getting estimates and confidence intervals for the
adjusted
  >> treatment means.
  >> In my case, Treatment is a factor having two levels (say, A and B).
  >> Is there a way to force lme() to report these quantities?
  >
  > If I understand what you are asking, you can get these standard errors
  > by modifying the formula to
  >
  > FollowUp ~ 0 + Treatment + Baseline
  >
  >


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